datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
gsm-hard
Dataset Summary
This is the harder version of gsm8k math reasoning dataset (https://huggingface.co/datasets/gsm8k).
We construct this dataset by replacing the numbers in the questions of GSM8K with larger numbers that are less common.
Supported Tasks and Leaderboards
This dataset is used to evaluate math reasoning
Languages
English - Numbers
Dataset Structure
dataset = load_dataset("reasoning-machines/gsm-hard")
DatasetDict({
train: Dataset({… See the full description on the dataset page: https://huggingface.co/datasets/reasoning-machines/gsm-hard.medical-o1-reasoning-SFT
News
[2025/04/22] We split the data and kept only the medical SFT dataset (medical_o1_sft.json). The file medical_o1_sft_mix.json contains a mix of medical and general instruction data.
[2025/02/22] We released the distilled dataset from Deepseek-R1 based on medical verifiable problems. You can use it to initialize your models with the reasoning chain from Deepseek-R1.
[2024/12/25] We open-sourced the medical reasoning dataset for SFT, built on medical verifiable problems and an… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/medical-o1-reasoning-SFT.natural_reasoningNaturalReasoning is a large-scale dataset for general reasoning tasks. It consists of high-quality challenging reasoning questions backtranslated from pretraining corpora DCLM and FineMath. The questions have been deduplicated and decontaminated from popular reasoning benchmarks including MATH, GPQA, MMLU-Pro, MMLU-STEM. For each question, we extract the reference final answer from the original document from the pretraining corpora if possible. We also provide a model-generated response from… See the full description on the dataset page: https://huggingface.co/datasets/facebook/natural_reasoning.kimi-cyber-reasoning
Kimi Cyber Reasoning
997 chain-of-thought records covering 13 cybersecurity disciplines and 4 systems engineering domains, distilled from the Kimi K3 reasoning model via API. Every record provides an explicit step-by-step <think> reasoning trace followed by a technical resolution, unified code diff fix, or structured tool invocation.
The dataset was curated as an anchor set for training, healing, and specializing compact reasoning models on systems security and tool calling… See the full description on the dataset page: https://huggingface.co/datasets/echel0nn1881/kimi-cyber-reasoning.VBVR-Bench-Data
VBVR: A Very Big Video Reasoning Suite
Overview
Video reasoning grounds intelligence in spatiotemporally consistent visual environments that go beyond what text can naturally capture,
enabling intuitive reasoning over motion, interaction, and causality. Rapid progress in video models has focused primarily on visual quality.
Systematically studying video reasoning and its scaling behavior suffers from a lack of… See the full description on the dataset page: https://huggingface.co/datasets/Video-Reason/VBVR-Bench-Data.Reason-RFT-CoT-Dataset
🤗 Reason-RFT CoT Dateset
The full dataset used in our project "Reason-RFT: Reinforcement Fine-Tuning for Visual Reasoning".
⭐️ Project │ 🌎 Github │ 🔥 Models │ 📑 ArXiv │ 💬 WeChat
🤖 RoboBrain: Aim to Explore ReasonRFT Paradigm to Enhance RoboBrain's Embodied Reasoning Capabilities.
♣️ Quick Start
Please refer to Dataset Preparation
🔥 Overview
Visual reasoning abilities play a crucial role in understanding complex multimodal… See the full description on the dataset page: https://huggingface.co/datasets/tanhuajie2001/Reason-RFT-CoT-Dataset.reasoningSuperior-Reasoning-SFT-gpt-oss-120b
Superior-Reasoning-SFT-gpt-oss-120b
📣 News
Our dataset ranked #1 on the Hugging Face Datasets Trending leaderboard from January 20 to January 30.
🚀 Overview
The Superior-Reasoning-SFT-gpt-oss-120b dataset is a high-quality, open-source collection containing 435K samples designed to democratize the training of high-performance Long Chain-of-Thought (Long-CoT) models. Unlike standard distilled datasets that rely on random sampling or… See the full description on the dataset page: https://huggingface.co/datasets/Alibaba-Apsara/Superior-Reasoning-SFT-gpt-oss-120b.GMAI-Reasoning10K
GMAI-Reasoning10K
Medical Reasoning dataset used in GMAI-VL-R1
Data description
GMAI-Reasoning10K is a high-quality medical image reasoning dataset containing 10,000 carefully selected samples. The data was collected from 95 medical datasets from reliable sources such as Kaggle, GrandChallenge, and Open-Release, covering 12 imaging modalities including X-ray, CT, and MRI.
Data preprocessing followed the standardization methods from SAMed-20M: 3D data (CT/MRI) had… See the full description on the dataset page: https://huggingface.co/datasets/General-Medical-AI/GMAI-Reasoning10K.Cybersecurity_Reasoning_Dataset
Cybersecurity Reasoning Dataset (Model-Agnostic)
A model-agnostic re-architecture of the Cybersecurity Reasoning Dataset. The original
corpus was format-bound to the Mistral/Llama ### Instruction: / ### Response: template;
this dataset losslessly separates reasoning content from format, providing one
neutral canonical corpus plus four per-family rendered training variants
(Mistral/Llama, DeepSeek, ChatML, Gemma).
Why this exists. Identical content scored 88.1 on a… See the full description on the dataset page: https://huggingface.co/datasets/dpevzner/Cybersecurity_Reasoning_Dataset.virl39k_reasoningreason-tool-use-demo-1500
Dataset info
The dataset is a selection of reasoning toolcalls data from https://huggingface.co/datasets/interstellarninja/hermes_reasoning_tool_use, which contains data from Hermes-Tools、Glaive-FC、ToolAce、Nvidia-When2Call.
The format has been transformed to adapt llama-factory v1 training pipeline.
Reasoning-Table
Reasoning-Table: Exploring Reinforcement Learning for Table Reasoning
The Reasoning-Table dataset is a high-quality, reasoning dataset designed for table reasoning tasks.
📁 Directory Structure
This repository is organized by task. Each subfolder contains task-specific reasoning data, including raw and filtered versions. Here is an overview:
├── fetaqa/
├── feverous/
├── finqa/
├── gsm8k/
├── hitab/
├── hybridqa/
├── multihierttt/
├── ottqa/
├── tabfact/
├── tatqa/
├──… See the full description on the dataset page: https://huggingface.co/datasets/TableQAKit/Reasoning-Table.claude-opus-4.6-4.7-reasoning-8.7k
Background
Ended up with some tokens to burn on a Claude Max plan. Assembly began during 4.6 and moved to 4.7. Model is tagged. The development evolved as it went along. The dataset has not been manually reviewed. It's entirely Claude developed.
Clarification on Reasoning
The reasoning is not Claude's actual chain-of-thought (cot) and is not summarized cot. It's a fully synthetic cot created as part of the Assistant response to mimic the type of "thinking"… See the full description on the dataset page: https://huggingface.co/datasets/angrygiraffe/claude-opus-4.6-4.7-reasoning-8.7k.GLM-5.1-Reasoning-1M-Cleaned
GLM-5.1-Reasoning-1M-Cleaned
GLM-5.1-Reasoning-1M-Cleaned is a cleaned and reformatted derivative of Kassadin88/GLM-5.1-1000000x. It preserves the original four-subset layout (main, PHD-Science, Multilingual-STEM, Math) while converting every example into a unified SFT-ready schema with explicit conversations, input, output, domain, and meta fields.
This release was prepared from the original dataset published by Kassadin88.
Summary
Teacher model in the data: GLM-5.1… See the full description on the dataset page: https://huggingface.co/datasets/Jackrong/GLM-5.1-Reasoning-1M-Cleaned.rollouts-olmo7b-cue-search
rollouts-olmo7b-cue-search
Model: allenai/Olmo-3-1025-7B (snapshot a81bae42).
Tokenizer: allenai/Olmo-3-1025-7B (snapshot a81bae42).
Protocol: RL-Zero prompt, MATH-500 x 4 rollouts, budget 31,744, T 0.6, top-p 0.95, seed 20260819 (depth-2 exhaustive and n-gram chain: seed 20260821); the top-20 beam nominee screen, ten random-opener arms, every depth-2 opener (84 shards, arm names unique across shards) and the n-gram chain arms.
Rollouts generated on the CSAIL cluster for the… See the full description on the dataset page: https://huggingface.co/datasets/reasoning-cues/rollouts-olmo7b-cue-search.python_functions_reasoningThis is the Python (functions) coding reasoning dataset used to train
Notbad v1.0 Mistral 24B reasoning model.
The reasoning data were sampled from an RL-based self-improved
Mistral-Small-24B-Instruct-2501 model.
The Python functions and instructions were sourced from OpenCoder Dataset Stage1
and from open source projects on Github.
You can try Notbad v1.0 Mistral 24B on chat.labml.ai.
Cosmos-Reason1-SFT-Dataset
Dataset Description:
The data format is a pair of video and text annotations. We summarize the data and annotations in Table 4 (SFT), Table 5 (RL), and Table 6 (Benchmark) of the Cosmos-Reason1 paper. We release the annotations for embodied reasoning tasks for BridgeDatav2, RoboVQA, Agibot, HoloAssist, AV, and the videos for the RoboVQA and AV datasets. We additionally release the annotations and videos for the RoboFail dataset for benchmarks. By releasing the dataset, NVIDIA… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Cosmos-Reason1-SFT-Dataset.Stitched-Reasoning-Trajectories-7M
Stitched-Reasoning-Trajectories-7M
Dataset Summary
Stitched-Reasoning-Trajectories-7M is a massive-scale, synthetic multi-hop reasoning dataset. It was built by algorithmically "stitching" together discrete reasoning traces from the original glaiveai/reasoning-v1-20m dataset into continuous, coherent, and logically structured multi-agent trajectories.
By extracting internal sub-questions from <think> blocks and mapping high-information keyword overlaps, this dataset… See the full description on the dataset page: https://huggingface.co/datasets/ajibawa-2023/Stitched-Reasoning-Trajectories-7M.embodied-spatial-reasoning
Embodied Spatial Reasoning Tasks
Dataset Description
This dataset is part of the embodied-spatial-reasoning project, where the agent has to actively explore the environment to determine if certain spatial relationships hold true. The tasks involve spatial reasoning with various objects and scenes. Each task includes a query about the spatial relationships between objects within a scene, which the agent must verify through exploration.
Dataset Structure
The… See the full description on the dataset page: https://huggingface.co/datasets/thanhqt2002/embodied-spatial-reasoning.gsm8k-multilingual-reasoning
gsm8k-multilingual-reasoning
GSM8K with reasoning translated to multiple languages
Schema
{"prompt": "...", "answer": "...", "reasoning": "...", "metadata": {...}}
Usage
from datasets importload_dataset
ds = load_dataset("eddie-OB/gsm8k-multilingual-reasoning")
print(ds["train"][0])
Source
Derived from OpenAI GSM8K.
math-reasoning-sft-100k
Math Reasoning SFT (100K)
100,000 math problems with detailed step-by-step solutions — ready for supervised fine-tuning of math reasoning models.
Dataset Description
100,000 problems across 8 mathematical categories and 3 difficulty levels:
Categories
Category
Examples
Topics
word_problems
~23,100
Rate/time/distance, work problems, mixture, meeting/catch-up
arithmetic
~15,400
Percentages, profit/loss, ratios
geometry
~15,400
Area… See the full description on the dataset page: https://huggingface.co/datasets/stindardlogic/math-reasoning-sft-100k.Kimi-K2.5-Reasoning-1M-Cleaned
🪐 Kimi-K2.5-Reasoning-1M-Cleaned
Kimi-K2.5-Reasoning-1M-Cleaned is a cleaned derivative of ianncity/KIMI-K2.5-1000000x. It preserves the original four-config layout from the source dataset and rewrites each record into a unified reasoning-SFT schema with id, conversations, input, output, domain, and meta.
Summary
Source dataset: ianncity/KIMI-K2.5-1000000x
Source author: ianncity
Teacher model recorded in meta.teacher_model: KIMI-K2.5
Token lengths computed with… See the full description on the dataset page: https://huggingface.co/datasets/Jackrong/Kimi-K2.5-Reasoning-1M-Cleaned.reason-embed-data
ReasonEmbed: Enhanced Text Embeddings for Reasoning-Intensive Document Retrieval
This repository contains the synthetic training data introduced in the paper ReasonEmbed: Enhanced Text Embeddings for Reasoning-Intensive Document Retrieval. The dataset is designed to enhance text embeddings for reasoning-intensive document retrieval tasks.
Dataset Overview
v0928
This version corresponds to the 81,659 training samples used in the paper. The… See the full description on the dataset page: https://huggingface.co/datasets/hanhainebula/reason-embed-data.reasoning-base-20k
Dataset Card for Reasoning Base 20k
Dataset Details
Dataset Description
This dataset is designed to train a reasoning model. That can think through complex problems before providing a response, similar to how a human would. The dataset includes a wide range of problems from various domains (science, coding, math, etc.), each with a detailed chain of thought (COT) and the correct answer. The goal is to enable the model to learn and refine its reasoning process… See the full description on the dataset page: https://huggingface.co/datasets/KingNish/reasoning-base-20k.claude-4.5-opus-high-reasoning-250xThis is a reasoning dataset created using Claude Opus 4.5 with a reasoning depth set to high. Some of these questions are from reedmayhew and the rest were generated.
The dataset is meant for creating distilled versions of Claude Opus 4.5 by fine-tuning already existing open-source LLMs.
Stats
Costs: $ 52.3 (USD)
Total tokens (input + output): 2.13 M
NOESIS-1M-reasoning-router-code-math-psych-opus47-deepseek4-qwen36-gemini31-r1-gpt54
⚡ Each donation funds the next large quant.
I host free GGUF or MoE quants as independent research.
Local hardware: Mechrevo Kuangshi GM7AG0M — RTX 3060 Laptop 6GB GDDR6, 64GB DDR5, i7-12700H (14C/20T, 4.7GHz), Windows 11, Samsung 990 Pro.
Good for imatrix and 0.6–35B-class work in RAM. 9B+ and searches need rented H200/Blackwell, typically $100 per quant.
🎉 Boosty🦄 |
☕ Buy Me a Coffee🦄 |
⭐ DonationAlerts🦄
💚 Thanks to Hugging Face for extra storage.🦄… See the full description on the dataset page: https://huggingface.co/datasets/AMAImedia/NOESIS-1M-reasoning-router-code-math-psych-opus47-deepseek4-qwen36-gemini31-r1-gpt54.context
On the Interplay of Pre-Training, Mid-Training, and RL on Reasoning Language Models
Charlie Zhang, Graham Neubig,
Xiang Yue
Carnegie Mellon University, Language Technologies Institute
Does Reinforcement Learning Truly Extend Reasoning?
This work explores the discrepancy in views on RL's effectiveness in extending language models' reasoning abilities. Some characterize RL as a capability refiner, while others see it as inducing new compositional skills. This challenge… See the full description on the dataset page: https://huggingface.co/datasets/Interplay-LM-Reasoning/context.composition
On the Interplay of Pre-Training, Mid-Training, and RL on Reasoning Language Models
Charlie Zhang, Graham Neubig,
Xiang Yue
Carnegie Mellon University, Language Technologies Institute
Does Reinforcement Learning Truly Extend Reasoning?
This work explores the discrepancy in views on RL's effectiveness in extending language models' reasoning abilities. Some characterize RL as a capability refiner, while others see it as inducing new compositional skills. This challenge… See the full description on the dataset page: https://huggingface.co/datasets/Interplay-LM-Reasoning/composition.Synthetic-Causal-Reasoning-50k
🏭 Sovereign Synthetic Reasoning Dataset (400k)
"High-Quality Chain-of-Thought Data at Scale."
📊 Overview
This dataset contains 400,000 synthetic reasoning samples spanning 16 enterprise domains (Finance, Pharma, Legal, Cybersecurity, Supply Chain, etc.).
It was generated using the Sovereign Generator, which produced 1.6 million samples and applied a strict quality filter (Top 25%) to retain only the most logically consistent and complex chains.
Average Quality… See the full description on the dataset page: https://huggingface.co/datasets/davidfoss/Synthetic-Causal-Reasoning-50k.